Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of Computation Physics and Earth Science
Rosyad, Muhammad Asyril Ar
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Physics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Perbandingan Metode Random Forest dan LSTM untuk Prediksi Suhu Rosyad, Muhammad Asyril Ar; Maghridlo, Aviv
Journal of Computation Physics and Earth Science (JoCPES) Vol 5 No 1 (2025): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63581/JoCPES.v5i1.09

Abstract

This study compares the performance of Long Short-Term Memory (LSTM) and Random Forest (RF) models in predicting temperature data from Tanjung Priok, Indonesia, using evaluation metrics such as RMSE, MAE, and R² Score. The LSTM model demonstrated its ability to capture temporal dependencies and temperature trends, achieving an R² score of 0.4493 and an MAE of 0.5863. In contrast, the RF model performed better in minimizing prediction errors, with a lower RMSE of 0.6498 and an R² score of 0.4066. While the LSTM model excelled in explaining variance in the temperature data, the RF model was more effective in stable periods, exhibiting lower prediction errors. The results highlight that both models have distinct advantages, with LSTM better suited for capturing long-term temperature trends and RF performing well during periods of stability. Future research could explore hybrid models or further optimization of these techniques to improve prediction accuracy, particularly for dynamic and extreme temperature fluctuations.
Co-Authors Maghridlo, Aviv